With recent advances in 3d electron microscopy, optogenetics and large-scale chronic in vivo neural imaging, it is now possible to measure and perturb the activity of large populations of neurons, and to map their connectivity. These new data can be used understand how the structure of a neural circuit gives rise to its function. I develop machine learning algorithms to map neural connectivity, characterize neural activity and models to relate activity to connectivity.

Biography

My PhD at MIT was supervised by Sebastian Seung during which I developed machine learning methods for reconstructing the connectivity of neural circuits from 3d electron microscopic datasets. Led by Winfried Denk, we used these methods to map the circuitry of the inner plexiform layer of the mouse retina. These methods are also the "AI" that power EyeWire, a citizen-science project led by the Sebastian Seung, which uses crowd-sourcing to map a much larger volume of the mouse retina, also imaged by the Denk lab.

During my postdoc at the incredible Gatsby Unit at University College London, I was advised by Peter Dayan and Michael Hausser. At UCL, I worked on building statistical models of large-scale neural activity recordings. I also drank a lot of tea.